
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers import AutoModelForCausalLM





@require_torch
class      lowercase__    (							unittest.TestCase  ):







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       lowerCAmelCase_					:       Optional[Any]    =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       Optional[Any]    =		-1

       lowerCAmelCase_					:       Any    =		ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       List[Any]    =		model.generate(lowerCamelCase_    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase_						)
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.decode(greedy_ids[0]						)

       with CaptureStdout() as cs:
            lowerCAmelCase_					:       Tuple    =		TextStreamer(lowerCamelCase_						)
            model.generate(lowerCamelCase_    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase_    ,						streamer=lowerCamelCase_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       lowerCAmelCase_					:       Tuple    =		cs.out[:-1]

       self.assertEqual(lowerCamelCase_    ,						lowerCamelCase_						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       lowerCAmelCase_					:       Union[str, Any]    =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       Tuple    =		-1

       lowerCAmelCase_					:       Dict    =		ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       List[Any]    =		model.generate(lowerCamelCase_    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase_						)
       lowerCAmelCase_					:       List[Any]    =		tokenizer.decode(greedy_ids[0]						)

       lowerCAmelCase_					:       Union[str, Any]    =		TextIteratorStreamer(lowerCamelCase_						)
       lowerCAmelCase_					:       List[Any]    =		{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
       lowerCAmelCase_					:       Optional[int]    =		Thread(target=model.generate    ,						kwargs=lowerCamelCase_						)
       thread.start()
       lowerCAmelCase_					:       Union[str, Any]    =		''''''
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(lowerCamelCase_    ,						lowerCamelCase_						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       lowerCAmelCase_					:       Tuple    =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       Dict    =		-1

       lowerCAmelCase_					:       List[str]    =		ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       Optional[Any]    =		model.generate(lowerCamelCase_    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase_						)
       lowerCAmelCase_					:       str    =		greedy_ids[:, input_ids.shape[1] :]
       lowerCAmelCase_					:       List[str]    =		tokenizer.decode(new_greedy_ids[0]						)

       with CaptureStdout() as cs:
            lowerCAmelCase_					:       int    =		TextStreamer(lowerCamelCase_    ,						skip_prompt=lowerCamelCase_						)
            model.generate(lowerCamelCase_    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase_    ,						streamer=lowerCamelCase_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       lowerCAmelCase_					:       List[str]    =		cs.out[:-1]

       self.assertEqual(lowerCamelCase_    ,						lowerCamelCase_						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		AutoTokenizer.from_pretrained("""distilgpt2"""						)
       lowerCAmelCase_					:       Tuple    =		AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       List[str]    =		-1

       lowerCAmelCase_					:       str    =		torch.ones((1, 5)    ,						device=lowerCamelCase_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            lowerCAmelCase_					:       Tuple    =		TextStreamer(lowerCamelCase_    ,						skip_special_tokens=lowerCamelCase_						)
            model.generate(lowerCamelCase_    ,						max_new_tokens=1    ,						do_sample=lowerCamelCase_    ,						streamer=lowerCamelCase_						)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       lowerCAmelCase_					:       str    =		cs.out[:-1]  # Remove the final "\n"
       lowerCAmelCase_					:       int    =		tokenizer(lowerCamelCase_    ,						return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape    ,						(1, 1)						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[Any]    =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       lowerCAmelCase_					:       Dict    =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       List[str]    =		-1

       lowerCAmelCase_					:       int    =		ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size						).to(lowerCamelCase_						)
       lowerCAmelCase_					:       List[Any]    =		TextIteratorStreamer(lowerCamelCase_    ,						timeout=0.001						)
       lowerCAmelCase_					:       Tuple    =		{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
       lowerCAmelCase_					:       Optional[Any]    =		Thread(target=model.generate    ,						kwargs=lowerCamelCase_						)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(lowerCamelCase_						):
            lowerCAmelCase_					:       List[Any]    =		''''''
            for new_text in streamer:
                 streamer_text += new_text







import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCAmelCase_					:   int 							=						get_logger(__name__)





class      lowercase__    (							enum.Enum  ):
  __UpperCamelCase	     =  """all_checks"""
  __UpperCamelCase	     =  """basic_checks"""
  __UpperCamelCase	     =  """no_checks"""





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict					,   _a   :					str=None			)	->			Optional[Any]:
     if expected_checksums is None:
          logger.info("""Unable to verify checksums."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreDownloadedFiles(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedDownloadedFile(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Union[str, Any]    =		[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     lowerCAmelCase_					:       Optional[Any]    =		""" for """ + verification_name if verification_name is not None else """"""
     if len(_a			) > 0:
          raise NonMatchingChecksumError(
              F'Checksums didn\'t match{for_verification_name}:\n'
              F'{bad_urls}\n'
              """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""			)
     logger.info("""All the checksums matched successfully""" + for_verification_name			)





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict			)	->			str:
     if expected_splits is None:
          logger.info("""Unable to verify splits sizes."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreSplits(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedSplits(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Optional[int]    =		[
         {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
         for name in expected_splits
         if expected_splits[name].num_examples != recorded_splits[name].num_examples
     ]
     if len(_a			) > 0:
          raise NonMatchingSplitsSizesError(str(_a			)			)
     logger.info("""All the splits matched successfully."""			)






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					bool = True			)	->			dict:
     if record_checksum:
          lowerCAmelCase_					:       int    =		shaaaa()
          with open(_a					,   """rb"""			) as f:
               for chunk in iter(lambda: f.read(1 << 20			)					,   B""""""			):
                    m.update(_a			)
               lowerCAmelCase_					:       List[str]    =		m.hexdigest()
     else:
          lowerCAmelCase_					:       Union[str, Any]    =		None
     return {"num_bytes": os.path.getsize(_a			), "checksum": checksum}







def 							_lowerCAmelCase (  _a   :					Tuple			)	->			Optional[int]:
     if dataset_size and config.IN_MEMORY_MAX_SIZE:
          return dataset_size < config.IN_MEMORY_MAX_SIZE
     else:
          return False
