

"""simple docstring"""





from __future__ import annotations




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			list[list[int]]	):
    __lowercase								=						TypeError(
        "Matrices must be formed from a list of zero or more lists containing at "
        "least one and the same number of values, each of which must be of type "
        "int or float."	)
    if len(UpperCAmelCase__	) != 0:
       __lowercase								=						len(rows[0]	)
       if cols == 0:
          raise error
       for row in rows:
          if len(UpperCAmelCase__	) != cols:
             raise error
          for value in row:
             if not isinstance(UpperCAmelCase__,					(int, float)	):
                raise error
       __lowercase								=						rows
    else:
       __lowercase								=						[]


 def 					_lowercase (			self							:			int	):
    return [[row[i] for row in self.rows] for i in range(len(self.rows[0]	)	)]


 @property
 def 					_lowercase (			self							:			str	):
    return len(self.rows	)


 @property
 def 					_lowercase (			self							:			str	):
    return len(self.rows[0]	)


 @property
 def 					_lowercase (			self							:			List[str]	):
    return (self.num_rows, self.num_columns)


 @property
 def 					_lowercase (			self							:			int	):
    return self.order[0] == self.order[1]


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						[
        [0 if column_num != row_num else 1 for column_num in range(self.num_rows	)]
        for row_num in range(self.num_rows	)
    ]
    return Matrix(UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[Any]	):
    if not self.is_square:
       return 0
    if self.order == (0, 0):
       return 1
    if self.order == (1, 1):
       return int(self.rows[0][0]	)
    if self.order == (2, 2):
       return int(
           (self.rows[0][0] * self.rows[1][1])
           - (self.rows[0][1] * self.rows[1][0])	)
    else:
       return sum(
           self.rows[0][column] * self.cofactors().rows[0][column]
           for column in range(self.num_columns	)	)


 def 					_lowercase (			self							:			Dict	):
    return bool(self.determinant()	)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int	):
    __lowercase								=						[
        [
            self.rows[other_row][other_column]
            for other_column in range(self.num_columns	)
            if other_column != column
        ]
        for other_row in range(self.num_rows	)
        if other_row != row
    ]
    return Matrix(UpperCAmelCase__	).determinant()


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int	):
    if (row + column) % 2 == 0:
       return self.get_minor(UpperCAmelCase__,					UpperCAmelCase__	)
    return -1 * self.get_minor(UpperCAmelCase__,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int]	):
    return Matrix(
        [
            [self.get_minor(UpperCAmelCase__,					UpperCAmelCase__	) for column in range(self.num_columns	)]
            for row in range(self.num_rows	)
        ]	)


 def 					_lowercase (			self							:			Tuple	):
    return Matrix(
        [
            [
                self.minors().rows[row][column]
                if (row + column) % 2 == 0
                else self.minors().rows[row][column] * -1
                for column in range(self.minors().num_columns	)
            ]
            for row in range(self.minors().num_rows	)
        ]	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						[
        [self.cofactors().rows[column][row] for column in range(self.num_columns	)]
        for row in range(self.num_rows	)
    ]
    return Matrix(UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.determinant()
    if not determinant:
       raise TypeError("Only matrices with a non-zero determinant have an inverse"	)
    return self.adjugate() * (1 / determinant)


 def __repr__(			self							:			List[str]	):
    return str(self.rows	)


 def __str__(			self							:			int	):
    if self.num_rows == 0:
       return "[]"
    if self.num_rows == 1:
       return "[[" + ". ".join(str(self.rows[0]	)	) + "]]"
    return (
        "["
        + "\n ".join(
            [
                "[" + ". ".join([str(UpperCAmelCase__	) for value in row]	) + ".]"
                for row in self.rows
            ]	)
        + "]"
    )


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			list[int],					UpperCAmelCase__							:			int | None = None	):
    __lowercase								=						TypeError("Row must be a list containing all ints and/or floats"	)
    if not isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       raise type_error
    for value in row:
       if not isinstance(UpperCAmelCase__,					(int, float)	):
          raise type_error
    if len(UpperCAmelCase__	) != self.num_columns:
       raise ValueError(
           "Row must be equal in length to the other rows in the matrix"	)
    if position is None:
       self.rows.append(UpperCAmelCase__	)
    else:
       __lowercase								=						self.rows[0:position] + [row] + self.rows[position:]


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			list[int],					UpperCAmelCase__							:			int | None = None	):
    __lowercase								=						TypeError(
        "Column must be a list containing all ints and/or floats"	)
    if not isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       raise type_error
    for value in column:
       if not isinstance(UpperCAmelCase__,					(int, float)	):
          raise type_error
    if len(UpperCAmelCase__	) != self.num_rows:
       raise ValueError(
           "Column must be equal in length to the other columns in the matrix"	)
    if position is None:
       __lowercase								=						[self.rows[i] + [column[i]] for i in range(self.num_rows	)]
    else:
       __lowercase								=						[
           self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
           for i in range(self.num_rows	)
       ]


 def __eq__(			self							:			List[Any],					UpperCAmelCase__							:			object	):
    if not isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       return NotImplemented
    return self.rows == other.rows


 def __ne__(			self							:			Any,					UpperCAmelCase__							:			object	):
    return not self == other


 def __neg__(			self							:			Tuple	):
    return self * -1


 def __add__(			self							:			List[Any],					UpperCAmelCase__							:			Matrix	):
    if self.order != other.order:
       raise ValueError("Addition requires matrices of the same order"	)
    return Matrix(
        [
            [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns	)]
            for i in range(self.num_rows	)
        ]	)


 def __sub__(			self							:			int,					UpperCAmelCase__							:			Matrix	):
    if self.order != other.order:
       raise ValueError("Subtraction requires matrices of the same order"	)
    return Matrix(
        [
            [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns	)]
            for i in range(self.num_rows	)
        ]	)


 def __mul__(			self							:			str,					UpperCAmelCase__							:			Matrix | int | float	):
    if isinstance(UpperCAmelCase__,					(int, float)	):
       return Matrix(
           [[int(element * other	) for element in row] for row in self.rows]	)
    elif isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       if self.num_columns != other.num_rows:
          raise ValueError(
              "The number of columns in the first matrix must "
              "be equal to the number of rows in the second"	)
       return Matrix(
           [
               [Matrix.dot_product(UpperCAmelCase__,					UpperCAmelCase__	) for column in other.columns()]
               for row in self.rows
           ]	)
    else:
       raise TypeError(
           "A Matrix can only be multiplied by an int, float, or another matrix"	)


 def __pow__(			self							:			Any,					UpperCAmelCase__							:			int	):
    if not isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       raise TypeError("A Matrix can only be raised to the power of an int"	)
    if not self.is_square:
       raise ValueError("Only square matrices can be raised to a power"	)
    if other == 0:
       return self.identity()
    if other < 0:
       if self.is_invertable():
          return self.inverse() ** (-other)
       raise ValueError(
           "Only invertable matrices can be raised to a negative power"	)
    __lowercase								=						self
    for _ in range(other - 1	):
       result *= self
    return result



 @classmethod
 def 					_lowercase (			cls							:			str,					UpperCAmelCase__							:			list[int],					UpperCAmelCase__							:			list[int]	):
    return sum(row[i] * column[i] for i in range(len(UpperCAmelCase__	)	)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()







"""simple docstring"""





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 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	XGLMConfig
 __UpperCAmelCase	:					Optional[Any]   		=	{}
 __UpperCAmelCase	:					Union[str, Any]   		=	"gelu"


 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=1_4,					UpperCAmelCase__							:			str=7,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[Any]=True,					UpperCAmelCase__							:			int=True,					UpperCAmelCase__							:			List[str]=9_9,					UpperCAmelCase__							:			Union[str, Any]=3_2,					UpperCAmelCase__							:			Union[str, Any]=2,					UpperCAmelCase__							:			Union[str, Any]=4,					UpperCAmelCase__							:			Tuple=3_7,					UpperCAmelCase__							:			List[Any]="gelu",					UpperCAmelCase__							:			List[str]=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Tuple=5_1_2,					UpperCAmelCase__							:			Optional[Any]=0.02,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						d_model
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						ffn_dim
    __lowercase								=						activation_function
    __lowercase								=						activation_dropout
    __lowercase								=						attention_dropout
    __lowercase								=						max_position_embeddings
    __lowercase								=						initializer_range
    __lowercase								=						None
    __lowercase								=						0
    __lowercase								=						2
    __lowercase								=						1


 def 					_lowercase (			self							:			Union[str, Any]	):
    return XGLMConfig.from_pretrained("facebook/xglm-564M"	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						tf.clip_by_value(
        ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	),					clip_value_min=0,					clip_value_max=3	)

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

    __lowercase								=						self.get_config()

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

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
    )


 def 					_lowercase (			self							:			List[Any]	):
    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=UpperCAmelCase__,					bos_token_id=self.bos_token_id,					eos_token_id=self.eos_token_id,					pad_token_id=self.pad_token_id,					return_dict=UpperCAmelCase__,					)



 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.prepare_config_and_inputs()

    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs

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

    return config, inputs_dict







@require_tf
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __UpperCAmelCase	:					List[str]   		=	(TFXGLMForCausalLM,) if is_tf_available() else ()
 __UpperCAmelCase	:					Any   		=	(
     {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __UpperCAmelCase	:					Optional[Any]   		=	False
 __UpperCAmelCase	:					List[str]   		=	False
 __UpperCAmelCase	:					int   		=	False


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						TFXGLMModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					n_embd=3_7	)


 def 					_lowercase (			self							:			Any	):
    self.config_tester.run_common_tests()


 @slow
 def 					_lowercase (			self							:			List[str]	):
    for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						TFXGLMModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."	)
 def 					_lowercase (			self							:			int	):
    super().test_resize_token_embeddings()







@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]=True	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]],					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
    __lowercase								=						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
    # fmt: on
    __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					num_beams=1	)
    if verify_outputs:
       self.assertListEqual(output_ids[0].numpy().tolist(),					UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)

    tf.random.set_seed(0	)
    __lowercase								=						tokenizer("Today is a nice day and",					return_tensors="tf"	)
    __lowercase								=						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"	):
       __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					seed=[7, 0]	)
    __lowercase								=						tokenizer.decode(output_ids[0],					skip_special_tokens=UpperCAmelCase__	)

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



 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)

    __lowercase								=						"left"

    # use different length sentences to test batching
    __lowercase								=						[
        "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",
    ]

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

    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					attention_mask=inputs["attention_mask"],					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[0],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[1],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

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

    __lowercase								=						[
        "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(UpperCAmelCase__,					UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__,					[non_padded_sentence, padded_sentence]	)




