



import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


lowercase_	       =			WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[int]	)  ->						str:
					__lowerCAmelCase	   =test_results.split(""" """	)

					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =0

					# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
					# When it is too long, those signs are not present.
					__lowerCAmelCase	   =expressions[-2] if """=""" in expressions[-1] else expressions[-1]

					for i, expression in enumerate(__lowerCamelCase	):
										if "failed" in expression:
															failed += int(expressions[i - 1]	)
										if "passed" in expression:
															success += int(expressions[i - 1]	)

					return failed, success, time_spent






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						Union[str, Any]:
					__lowerCAmelCase	   ={}
					__lowerCAmelCase	   =None
					__lowerCAmelCase	   =False
					for line in failures_short_lines.split("""\n"""	):
										if re.search(r"""_ \[doctest\]"""				,   __lowerCamelCase	):
															__lowerCAmelCase	   =True
															__lowerCAmelCase	   =line.split(""" """	)[2]
										elif in_error and not line.split(""" """	)[0].isdigit():
															__lowerCAmelCase	   =line
															__lowerCAmelCase	   =False

					return failures






class 						__a :



	def __init__(						self							:    Any				,	snake_case_							:    str				,	snake_case_							:    Dict)->							Optional[Any]:
						__lowerCAmelCase	   =title

						__lowerCAmelCase	   =doc_test_results["""time_spent"""].split(""",""")[0]
						__lowerCAmelCase	   =doc_test_results["""success"""]
						__lowerCAmelCase	   =doc_test_results["""failures"""]
						__lowerCAmelCase	   =self.n_success + self.n_failures

						# Failures and success of the modeling tests
						__lowerCAmelCase	   =doc_test_results



	@property
	def 		UpperCamelCase							(						self							:    List[Any])->							str:
						__lowerCAmelCase	   =[self._time_spent]
						__lowerCAmelCase	   =0

						for time in time_spent:
											__lowerCAmelCase	   =time.split(""":""")

											# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
											if len(snake_case_) == 1:
																__lowerCAmelCase	   =[0, 0, time_parts[0]]

											__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =int(time_parts[0]), int(time_parts[1]), float(time_parts[2])
											total_secs += hours * 36_00 + minutes * 60 + seconds

						__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
						return F"""{int(snake_case_)}h{int(snake_case_)}m{int(snake_case_)}s"""



	@property
	def 		UpperCamelCase							(						self							:    Any)->							Dict:
						return {"type": "header", "text": {"type": "plain_text", "text": self.title}}



	@property
	def 		UpperCamelCase							(						self							:    int)->							Dict:
						return {
						    "type": "section",
						    "text": {
						        "type": "plain_text",
						        "text": F"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
						        "emoji": True,
						    },
						    "accessory": {
						        "type": "button",
						        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
						        "url": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
						    },
						}



	@property
	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						return {
						    "type": "section",
						    "text": {
						        "type": "plain_text",
						        "text": (
						            F"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
						            F""" {self.time}."""
						        ),
						        "emoji": True,
						    },
						    "accessory": {
						        "type": "button",
						        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
						        "url": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
						    },
						}



	@property
	def 		UpperCamelCase							(						self							:    int)->							Dict:
						__lowerCAmelCase	   =40
						__lowerCAmelCase	   ={k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(snake_case_				,	snake_case_)}

						__lowerCAmelCase	   =""""""
						for category, failures in category_failures.items():
											if len(snake_case_) == 0:
																continue

											if report != "":
																report += "\n\n"

											report += F"""*{category} failures*:""".ljust(line_length // 2).rjust(line_length // 2) + "\n"
											report += "`"
											report += "`\n`".join(snake_case_)
											report += "`"

						return {
						    "type": "section",
						    "text": {
						        "type": "mrkdwn",
						        "text": F"""The following examples had failures:\n\n\n{report}\n""",
						    },
						}



	@property
	def 		UpperCamelCase							(						self							:    Any)->							str:
						__lowerCAmelCase	   =[self.header]

						if self.n_failures > 0:
											blocks.append(self.failures)

						if self.n_failures > 0:
											blocks.extend([self.category_failures])

						if self.n_failures == 0:
											blocks.append(self.no_failures)

						return json.dumps(snake_case_)



	@staticmethod
	def 		UpperCamelCase							(						)->							Dict:
						__lowerCAmelCase	   =[
						    {
						        """type""": """section""",
						        """text""": {
						            """type""": """plain_text""",
						            """text""": """There was an issue running the tests.""",
						        },
						        """accessory""": {
						            """type""": """button""",
						            """text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
						            """url""": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
						        },
						    }
						]

						print("""Sending the following payload""")
						print(json.dumps({"""blocks""": json.loads(snake_case_)}))

						client.chat_postMessage(
						    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]				,	text="""There was an issue running the tests."""				,	blocks=snake_case_				,	)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Dict:
						print("""Sending the following payload""")
						print(json.dumps({"""blocks""": json.loads(self.payload)}))

						__lowerCAmelCase	   =F"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else """All tests passed."""

						__lowerCAmelCase	   =client.chat_postMessage(
						    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]				,	blocks=self.payload				,	text=snake_case_				,	)



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    str)->							Union[str, Any]:
						__lowerCAmelCase	   =""""""
						for key, value in failures.items():
											__lowerCAmelCase	   =value[:2_00] + """ [Truncated]""" if len(snake_case_) > 2_50 else value
											failures_text += F"""*{key}*\n_{value}_\n\n"""

						__lowerCAmelCase	   =job_name
						__lowerCAmelCase	   ={"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

						if job_link is not None:
											__lowerCAmelCase	   ={
											    """type""": """button""",
											    """text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
											    """url""": job_link,
											}

						return [
						    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
						    content,
						    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
						]



	def 		UpperCamelCase							(						self							:    Dict)->							Any:
						if self.thread_ts is None:
											raise ValueError("""Can only post reply if a post has been made.""")

						__lowerCAmelCase	   =self.doc_test_results.pop("""job_link""")
						self.doc_test_results.pop("""failures""")
						self.doc_test_results.pop("""success""")
						self.doc_test_results.pop("""time_spent""")

						__lowerCAmelCase	   =sorted(self.doc_test_results.items()				,	key=lambda snake_case_: t[0])
						for job, job_result in sorted_dict:
											if len(job_result["""failures"""]):
																__lowerCAmelCase	   =F"""*Num failures* :{len(job_result['failed'])} \n"""
																__lowerCAmelCase	   =job_result["""failures"""]
																__lowerCAmelCase	   =self.get_reply_blocks(snake_case_				,	snake_case_				,	snake_case_				,	text=snake_case_)

																print("""Sending the following reply""")
																print(json.dumps({"""blocks""": blocks}))

																client.chat_postMessage(
																    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]				,	text=F"""Results for {job}"""				,	blocks=snake_case_				,	thread_ts=self.thread_ts["""ts"""]				,	)

																time.sleep(1)






def 					__lowerCAmelCase		(  )  ->						str:
					__lowerCAmelCase	   =os.environ["""GITHUB_RUN_ID"""]
					__lowerCAmelCase	   =f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
					__lowerCAmelCase	   =requests.get(__lowerCamelCase	).json()
					__lowerCAmelCase	   ={}

					try:
										jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}	)
										__lowerCAmelCase	   =math.ceil((result["""total_count"""] - 100) / 100	)

										for i in range(__lowerCamelCase	):
															__lowerCAmelCase	   =requests.get(url + f"""&page={i + 2}"""	).json()
															jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}	)

										return jobs
					except Exception as e:
										print("""Unknown error, could not fetch links."""				,   __lowerCamelCase	)

					return {}






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						int:
					__lowerCAmelCase	   ={}

					if os.path.exists(__lowerCamelCase	):
										__lowerCAmelCase	   =os.listdir(__lowerCamelCase	)
										for file in files:
															try:
																				with open(os.path.join(__lowerCamelCase				,   __lowerCamelCase	)				,   encoding="""utf-8"""	) as f:
																									__lowerCAmelCase	   =f.read()
															except UnicodeDecodeError as e:
																				raise ValueError(f"""Could not open {os.path.join(__lowerCamelCase				,   __lowerCamelCase	)}."""	) from e

					return _artifact






def 					__lowerCAmelCase		(  )  ->						Optional[int]:






					class 						__a :



						def __init__(						self							:    str				,	snake_case_							:    str)->							Optional[Any]:
											__lowerCAmelCase	   =name
											__lowerCAmelCase	   =[]



						def __str__(						self							:    Dict)->							Tuple:
											return self.name



						def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    str)->							Optional[Any]:
											self.paths.append({"""name""": self.name, """path""": path})


					__lowerCAmelCase	   ={}

					__lowerCAmelCase	   =filter(os.path.isdir				,   os.listdir()	)
					for directory in directories:
										__lowerCAmelCase	   =directory
										if artifact_name not in _available_artifacts:
															__lowerCAmelCase	   =Artifact(__lowerCamelCase	)

															_available_artifacts[artifact_name].add_path(__lowerCamelCase	)

					return _available_artifacts


if __name__ == "__main__":
				lowercase_	       =			get_job_links()
				lowercase_	       =			retrieve_available_artifacts()

				lowercase_	       =			collections.OrderedDict(
				    [
				        ('''*.py''', '''API Examples'''),
				        ('''*.md''', '''MD Examples'''),
				    ]
				)

				# This dict will contain all the information relative to each doc test category:
				# - failed: list of failed tests
				# - failures: dict in the format 'test': 'error_message'
				lowercase_	       =			{
				    v: {
				        '''failed''': [],
				        '''failures''': {},
				    }
				    for v in docs.values()
				}

				# Link to the GitHub Action job
				lowercase_	       =			github_actions_job_links.get('''run_doctests''')

				lowercase_	       =			available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
				lowercase_	       =			retrieve_artifact(artifact_path['''name'''])
				if "stats" in artifact:
								lowercase_       ,       lowercase_       ,       lowercase_	       =			handle_test_results(artifact['''stats'''])
								lowercase_	       =			failed
								lowercase_	       =			success
								lowercase_	       =			time_spent[1:-1] + ''', '''

								lowercase_	       =			extract_first_line_failure(artifact['''failures_short'''])
								for line in artifact["summary_short"].split('''\n'''):
												if re.search('''FAILED''', line):
																lowercase_	       =			line.replace('''FAILED ''', '''''')
																lowercase_	       =			line.split()[0].replace('''\n''', '''''')

																if "::" in line:
																				lowercase_       ,       lowercase_	       =			line.split('''::''')
																else:
																				lowercase_       ,       lowercase_	       =			line, line

																for file_regex in docs.keys():
																				if fnmatch(file_path, file_regex):
																								lowercase_	       =			docs[file_regex]
																								doc_test_results[category]["failed"].append(test)

																								lowercase_	       =			all_failures[test] if test in all_failures else '''N/A'''
																								lowercase_	       =			failure
																								break

				lowercase_	       =			Message('''🤗 Results of the doc tests.''', doc_test_results)
				message.post()
				message.post_reply()









import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    XLMForMultipleChoice,
				    XLMForQuestionAnswering,
				    XLMForQuestionAnsweringSimple,
				    XLMForSequenceClassification,
				    XLMForTokenClassification,
				    XLMModel,
				    XLMWithLMHeadModel,
				)
				from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST






class 						__a :



	def __init__(						self							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    Optional[Any]=13				,	snake_case_							:    List[str]=7				,	snake_case_							:    Tuple=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    str=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    List[Any]=False				,	snake_case_							:    str=False				,	snake_case_							:    List[str]=False				,	snake_case_							:    List[Any]=2				,	snake_case_							:    List[Any]=99				,	snake_case_							:    Tuple=0				,	snake_case_							:    Optional[int]=32				,	snake_case_							:    Union[str, Any]=5				,	snake_case_							:    Any=4				,	snake_case_							:    str=0.1				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Optional[Any]=5_12				,	snake_case_							:    str=2				,	snake_case_							:    List[str]=0.0_2				,	snake_case_							:    Optional[int]=2				,	snake_case_							:    Union[str, Any]=4				,	snake_case_							:    str="last"				,	snake_case_							:    Union[str, Any]=True				,	snake_case_							:    int=None				,	snake_case_							:    Any=0				,	)->							Tuple:
						__lowerCAmelCase	   =parent
						__lowerCAmelCase	   =batch_size
						__lowerCAmelCase	   =seq_length
						__lowerCAmelCase	   =is_training
						__lowerCAmelCase	   =use_input_lengths
						__lowerCAmelCase	   =use_token_type_ids
						__lowerCAmelCase	   =use_labels
						__lowerCAmelCase	   =gelu_activation
						__lowerCAmelCase	   =sinusoidal_embeddings
						__lowerCAmelCase	   =causal
						__lowerCAmelCase	   =asm
						__lowerCAmelCase	   =n_langs
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =n_special
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =hidden_dropout_prob
						__lowerCAmelCase	   =attention_probs_dropout_prob
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =type_sequence_label_size
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =num_labels
						__lowerCAmelCase	   =num_choices
						__lowerCAmelCase	   =summary_type
						__lowerCAmelCase	   =use_proj
						__lowerCAmelCase	   =scope
						__lowerCAmelCase	   =bos_token_id



	def 		UpperCamelCase							(						self							:    Tuple)->							int:
						__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)
						__lowerCAmelCase	   =random_attention_mask([self.batch_size, self.seq_length])

						__lowerCAmelCase	   =None
						if self.use_input_lengths:
											__lowerCAmelCase	   =(
											    ids_tensor([self.batch_size]				,	vocab_size=2) + self.seq_length - 2
											)  # small variation of seq_length

						__lowerCAmelCase	   =None
						if self.use_token_type_ids:
											__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.n_langs)

						__lowerCAmelCase	   =None
						__lowerCAmelCase	   =None
						__lowerCAmelCase	   =None
						if self.use_labels:
											__lowerCAmelCase	   =ids_tensor([self.batch_size]				,	self.type_sequence_label_size)
											__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels)
											__lowerCAmelCase	   =ids_tensor([self.batch_size]				,	2).float()
											__lowerCAmelCase	   =ids_tensor([self.batch_size]				,	self.num_choices)

						__lowerCAmelCase	   =self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)



	def 		UpperCamelCase							(						self							:    Tuple)->							Union[str, Any]:
						return XLMConfig(
						    vocab_size=self.vocab_size				,	n_special=self.n_special				,	emb_dim=self.hidden_size				,	n_layers=self.num_hidden_layers				,	n_heads=self.num_attention_heads				,	dropout=self.hidden_dropout_prob				,	attention_dropout=self.attention_probs_dropout_prob				,	gelu_activation=self.gelu_activation				,	sinusoidal_embeddings=self.sinusoidal_embeddings				,	asm=self.asm				,	causal=self.causal				,	n_langs=self.n_langs				,	max_position_embeddings=self.max_position_embeddings				,	initializer_range=self.initializer_range				,	summary_type=self.summary_type				,	use_proj=self.use_proj				,	num_labels=self.num_labels				,	bos_token_id=self.bos_token_id				,	)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	)->							Tuple:
						__lowerCAmelCase	   =XLMModel(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	lengths=snake_case_				,	langs=snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	langs=snake_case_)
						__lowerCAmelCase	   =model(snake_case_)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    Dict				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    int				,	)->							Dict:
						__lowerCAmelCase	   =XLMWithLMHeadModel(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.loss.shape				,	())
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size))



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    int				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Dict:
						__lowerCAmelCase	   =XLMForQuestionAnsweringSimple(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)

						__lowerCAmelCase	   =model(snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_)
						__lowerCAmelCase	   =outputs
						self.parent.assertEqual(result.start_logits.shape				,	(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape				,	(self.batch_size, self.seq_length))



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    int				,	snake_case_							:    int				,	snake_case_							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Optional[int]:
						__lowerCAmelCase	   =XLMForQuestionAnswering(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)

						__lowerCAmelCase	   =model(
						    snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	cls_index=snake_case_				,	is_impossible=snake_case_				,	p_mask=snake_case_				,	)

						__lowerCAmelCase	   =model(
						    snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	cls_index=snake_case_				,	is_impossible=snake_case_				,	)

						((__lowerCAmelCase)   ,	)	   =result_with_labels.to_tuple()

						__lowerCAmelCase	   =model(snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_)

						((__lowerCAmelCase)   ,	)	   =result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape				,	())
						self.parent.assertEqual(result.start_top_log_probs.shape				,	(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(result.start_top_index.shape				,	(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(
						    result.end_top_log_probs.shape				,	(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(
						    result.end_top_index.shape				,	(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(result.cls_logits.shape				,	(self.batch_size,))



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    Optional[int]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    Optional[Any]				,	)->							int:
						__lowerCAmelCase	   =XLMForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.loss.shape				,	())
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size))



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    int				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	)->							Optional[Any]:
						__lowerCAmelCase	   =self.num_labels
						__lowerCAmelCase	   =XLMForTokenClassification(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels))



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[int]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    Tuple				,	snake_case_							:    List[str]				,	snake_case_							:    Tuple				,	)->							List[Any]:
						__lowerCAmelCase	   =self.num_choices
						__lowerCAmelCase	   =XLMForMultipleChoice(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =input_ids.unsqueeze(1).expand(-1				,	self.num_choices				,	-1).contiguous()
						__lowerCAmelCase	   =token_type_ids.unsqueeze(1).expand(-1				,	self.num_choices				,	-1).contiguous()
						__lowerCAmelCase	   =input_mask.unsqueeze(1).expand(-1				,	self.num_choices				,	-1).contiguous()
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_				,	)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices))



	def 		UpperCamelCase							(						self							:    Optional[Any])->							List[Any]:
						__lowerCAmelCase	   =self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	
						)	   =config_and_inputs
						__lowerCAmelCase	   ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
						return config, inputs_dict






@require_torch
class 						__a (     SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE       =       (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	SCREAMING_SNAKE_CASE       =       (
	    {
	        "feature-extraction": XLMModel,
	        "fill-mask": XLMWithLMHeadModel,
	        "question-answering": XLMForQuestionAnsweringSimple,
	        "text-classification": XLMForSequenceClassification,
	        "text-generation": XLMWithLMHeadModel,
	        "token-classification": XLMForTokenClassification,
	        "zero-shot": XLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    str)->							Tuple:
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("""Fast""")
						):
											# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
											# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
											# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
											return True

						return False



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Tuple				,	snake_case_							:    Any				,	snake_case_							:    str=False)->							str:
						__lowerCAmelCase	   =super()._prepare_for_class(snake_case_				,	snake_case_				,	return_labels=snake_case_)

						if return_labels:
											if model_class.__name__ == "XLMForQuestionAnswering":
																__lowerCAmelCase	   =torch.zeros(
																    self.model_tester.batch_size				,	dtype=torch.long				,	device=snake_case_)
																__lowerCAmelCase	   =torch.zeros(
																    self.model_tester.batch_size				,	dtype=torch.long				,	device=snake_case_)

						return inputs_dict



	def 		UpperCamelCase							(						self							:    int)->							List[Any]:
						__lowerCAmelCase	   =XLMModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_				,	emb_dim=37)



	def 		UpperCamelCase							(						self							:    List[str])->							Any:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase							(						self							:    Optional[int])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*snake_case_)



	def 		UpperCamelCase							(						self							:    str)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    int				,	snake_case_							:    Optional[int]=False				,	snake_case_							:    List[Any]=1)->							Optional[Any]:
						self.assertIsInstance(snake_case_				,	snake_case_)
						self.assertListEqual(
						    [isinstance(snake_case_				,	snake_case_) for iter_attentions in attentions]				,	[True] * len(snake_case_))
						self.assertEqual(len(snake_case_)				,	(max_length - min_length) * num_beam_groups)

						for idx, iter_attentions in enumerate(snake_case_):
											# adds PAD dummy token
											__lowerCAmelCase	   =min_length + idx + 1
											__lowerCAmelCase	   =min_length + idx + 1

											__lowerCAmelCase	   =(
											    batch_size * num_beam_groups,
											    config.num_attention_heads,
											    tgt_len,
											    src_len,
											)
											# check attn size
											self.assertListEqual(
											    [layer_attention.shape for layer_attention in iter_attentions]				,	[expected_shape] * len(snake_case_))



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[str]=False				,	snake_case_							:    Any=1)->							Any:
						self.assertIsInstance(snake_case_				,	snake_case_)
						self.assertListEqual(
						    [isinstance(snake_case_				,	snake_case_) for iter_hidden_states in hidden_states]				,	[True] * len(snake_case_)				,	)
						self.assertEqual(len(snake_case_)				,	(max_length - min_length) * num_beam_groups)

						for idx, iter_hidden_states in enumerate(snake_case_):
											# adds PAD dummy token
											__lowerCAmelCase	   =min_length + idx + 1
											__lowerCAmelCase	   =(batch_size * num_beam_groups, seq_len, config.hidden_size)
											# check hidden size
											self.assertListEqual(
											    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,	[expected_shape] * len(snake_case_)				,	)
						pass



	@slow
	def 		UpperCamelCase							(						self							:    Any)->							List[str]:
						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase	   =XLMModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)






@require_torch
class 						__a (     unittest.TestCase				):



	@slow
	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						__lowerCAmelCase	   =XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""")
						model.to(snake_case_)
						__lowerCAmelCase	   =torch.tensor([[14, 4_47]]				,	dtype=torch.long				,	device=snake_case_)  # the president
						__lowerCAmelCase	   =[
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						__lowerCAmelCase	   =model.generate(snake_case_				,	do_sample=snake_case_)
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,	snake_case_)




