



from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.xglm.modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				)







@require_tf
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       XGLMConfig
					lowerCamelCase     :     Optional[int]			    =       {}
					lowerCamelCase     :     Union[str, Any]			    =       'gelu'





					def __init__(						self    :  Dict ,					__lowercase    :  List[Any] ,					__lowercase    :  str=14 ,					__lowercase    :  str=7 ,					__lowercase    :  List[str]=True ,					__lowercase    :  Optional[int]=True ,					__lowercase    :  Union[str, Any]=True ,					__lowercase    :  str=99 ,					__lowercase    :  Optional[int]=32 ,					__lowercase    :  Optional[int]=2 ,					__lowercase    :  List[str]=4 ,					__lowercase    :  Dict=37 ,					__lowercase    :  Optional[Any]="gelu" ,					__lowercase    :  List[str]=0.1 ,					__lowercase    :  List[Any]=0.1 ,					__lowercase    :  Tuple=5_12 ,					__lowercase    :  Union[str, Any]=0.02 ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     parent
						UpperCAmelCase_								=     batch_size
						UpperCAmelCase_								=     seq_length
						UpperCAmelCase_								=     is_training
						UpperCAmelCase_								=     use_input_mask
						UpperCAmelCase_								=     use_labels
						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     d_model
						UpperCAmelCase_								=     num_hidden_layers
						UpperCAmelCase_								=     num_attention_heads
						UpperCAmelCase_								=     ffn_dim
						UpperCAmelCase_								=     activation_function
						UpperCAmelCase_								=     activation_dropout
						UpperCAmelCase_								=     attention_dropout
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     0
						UpperCAmelCase_								=     2
						UpperCAmelCase_								=     1





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						return XGLMConfig.from_pretrained("""facebook/xglm-564M"""		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     tf.clip_by_value(
						    ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size		) ,					clip_value_min=0 ,					clip_value_max=3		)

						UpperCAmelCase_								=     None
						if self.use_input_mask:
							UpperCAmelCase_								=     random_attention_mask([self.batch_size, self.seq_length]		)

						UpperCAmelCase_								=     self.get_config()

						UpperCAmelCase_								=     floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,					2		)

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						return XGLMConfig(
						    vocab_size=self.vocab_size ,					d_model=self.hidden_size ,					num_layers=self.num_hidden_layers ,					attention_heads=self.num_attention_heads ,					ffn_dim=self.ffn_dim ,					activation_function=self.activation_function ,					activation_dropout=self.activation_dropout ,					attention_dropout=self.attention_dropout ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					use_cache=__lowercase ,					bos_token_id=self.bos_token_id ,					eos_token_id=self.eos_token_id ,					pad_token_id=self.pad_token_id ,					return_dict=__lowercase ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.prepare_config_and_inputs()

						(
						    (
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,		
						)								=     config_and_inputs

						UpperCAmelCase_								=     {
						    """input_ids""": input_ids,
						    """head_mask""": head_mask,
						}

						return config, inputs_dict







@require_tf
class   _UpperCamelCase  (       A_						,       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Optional[Any]			    =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					lowerCamelCase     :     Optional[int]			    =       (TFXGLMForCausalLM,) if is_tf_available() else ()
					lowerCamelCase     :     Dict			    =       (
					    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
					)
					lowerCamelCase     :     Dict			    =       False
					lowerCamelCase     :     List[str]			    =       False
					lowerCamelCase     :     Tuple			    =       False





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFXGLMModelTester(self		)
						UpperCAmelCase_								=     ConfigTester(self ,					config_class=__lowercase ,					n_embd=37		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						self.config_tester.run_common_tests()





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     TFXGLMModel.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)





					@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""		)
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						super().test_resize_token_embeddings()







@require_tf
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  str=True		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
						UpperCAmelCase_								=     tf.convert_to_tensor([[2, 2_68, 98_65]] ,					dtype=tf.intaa		)  # The dog
						# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
						# fmt: off
						UpperCAmelCase_								=     [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
						# fmt: on
						UpperCAmelCase_								=     model.generate(__lowercase ,					do_sample=__lowercase ,					num_beams=1		)
						if verify_outputs:
							self.assertListEqual(output_ids[0].numpy().tolist() ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)
						UpperCAmelCase_								=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)

						tf.random.set_seed(0		)
						UpperCAmelCase_								=     tokenizer("""Today is a nice day and""" ,					return_tensors="""tf"""		)
						UpperCAmelCase_								=     tokenized.input_ids
						# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
						with tf.device(""":/CPU:0"""		):
							UpperCAmelCase_								=     model.generate(__lowercase ,					do_sample=__lowercase ,					seed=[7, 0]		)
						UpperCAmelCase_								=     tokenizer.decode(output_ids[0] ,					skip_special_tokens=__lowercase		)

						UpperCAmelCase_								=     (
						    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
						)
						self.assertEqual(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
						UpperCAmelCase_								=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)

						UpperCAmelCase_								=     """left"""

						# use different length sentences to test batching
						UpperCAmelCase_								=     [
						    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
						    """left-padding, such as in batched generation. The output for the sequence below should be the same """
						    """regardless of whether left padding is applied or not. When""",
						    """Hello, my dog is a little""",
						]

						UpperCAmelCase_								=     tokenizer(__lowercase ,					return_tensors="""tf""" ,					padding=__lowercase		)
						UpperCAmelCase_								=     inputs["""input_ids"""]

						UpperCAmelCase_								=     model.generate(input_ids=__lowercase ,					attention_mask=inputs["""attention_mask"""] ,					max_new_tokens=12		)

						UpperCAmelCase_								=     tokenizer(sentences[0] ,					return_tensors="""tf"""		).input_ids
						UpperCAmelCase_								=     model.generate(input_ids=__lowercase ,					max_new_tokens=12		)

						UpperCAmelCase_								=     tokenizer(sentences[1] ,					return_tensors="""tf"""		).input_ids
						UpperCAmelCase_								=     model.generate(input_ids=__lowercase ,					max_new_tokens=12		)

						UpperCAmelCase_								=     tokenizer.batch_decode(__lowercase ,					skip_special_tokens=__lowercase		)
						UpperCAmelCase_								=     tokenizer.decode(output_non_padded[0] ,					skip_special_tokens=__lowercase		)
						UpperCAmelCase_								=     tokenizer.decode(output_padded[0] ,					skip_special_tokens=__lowercase		)

						UpperCAmelCase_								=     [
						    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
						    """left-padding, such as in batched generation. The output for the sequence below should be the same """
						    """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
						    """a single""",
						    """Hello, my dog is a little bit of a shy one, but he is very friendly""",
						]
						self.assertListEqual(__lowercase ,					__lowercase		)
						self.assertListEqual(__lowercase ,					[non_padded_sentence, padded_sentence]		)









from __future__ import annotations

UpperCamelCase__   : str					  = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def    A_(			A				,      A				,      A				,      A				,      A				,      ):
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the reference grid
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the action grid

	UpperCAmelCase_								=     init[0]
	UpperCAmelCase_								=     init[1]
	UpperCAmelCase_								=     0
	UpperCAmelCase_								=     g + heuristic[x][y]  # cost from starting cell to destination cell
	UpperCAmelCase_								=     [[f, g, x, y]]

	UpperCAmelCase_								=     False  # flag that is set when search is complete
	UpperCAmelCase_								=     False  # flag set if we can't find expand

	while not found and not resign:
		if len(A   ) == 0:
			raise ValueError("""Algorithm is unable to find solution"""   )
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			UpperCAmelCase_								=     cell.pop()
			UpperCAmelCase_								=     next_cell[2]
			UpperCAmelCase_								=     next_cell[3]
			UpperCAmelCase_								=     next_cell[1]

			if x == goal[0] and y == goal[1]:
				UpperCAmelCase_								=     True
			else:
				for i in range(len(A   )   ):  # to try out different valid actions
					UpperCAmelCase_								=     x + DIRECTIONS[i][0]
					UpperCAmelCase_								=     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(A   ) and ya >= 0 and ya < len(grid[0]   ):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							UpperCAmelCase_								=     g + cost
							UpperCAmelCase_								=     ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]   )
							UpperCAmelCase_								=     1
							UpperCAmelCase_								=     i
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     goal[0]
	UpperCAmelCase_								=     goal[1]
	invpath.append([x, y]   )  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		UpperCAmelCase_								=     x - DIRECTIONS[action[x][y]][0]
		UpperCAmelCase_								=     y - DIRECTIONS[action[x][y]][1]
		UpperCAmelCase_								=     xa
		UpperCAmelCase_								=     ya
		invpath.append([x, y]   )

	UpperCAmelCase_								=     []
	for i in range(len(A   )   ):
		path.append(invpath[len(A   ) - 1 - i]   )
	return path, action


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = [
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				UpperCamelCase__   : Dict					  = [0, 0]
				# all coordinates are given in format [y,x]
				UpperCamelCase__   : Optional[int]					  = [len(grid) - 1, len(grid[0]) - 1]
				UpperCamelCase__   : Optional[Any]					  = 1

				# the cost map which pushes the path closer to the goal
				UpperCamelCase__   : Any					  = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												UpperCamelCase__   : Any					  = abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																UpperCamelCase__   : Dict					  = 99

				UpperCamelCase__  ,			UpperCamelCase__   : Dict					  = search(grid, init, goal, cost, heuristic)

				print("""ACTION MAP""")
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])


